Zhang, Z;
Thwaites, A;
Woolgar, A;
Moore, B;
Zhang, C;
(2025)
SWIM: Short-Window CNN Integrated With Mamba for EEG-Based Auditory Spatial Attention Decoding.
In:
Proceedings of 2024 IEEE Spoken Language Technology Workshop, SLT 2024.
(pp. pp. 1031-1038).
IEEE: Macao.
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Abstract
In complex auditory environments, the human auditory system possesses the remarkable ability to focus on a specific speaker while disregarding others. In this study, a new model named SWIM, a short-window convolution neural network (CNN) integrated with Mamba, is proposed for identifying the locus of auditory attention (left or right) from electroencephalography (EEG) signals without relying on speech envelopes. SWIM consists of two parts. The first is a short-window CNN (SWCNN), which acts as a short-term EEG feature extractor and achieves a final accuracy of 84.9% in the leave-one-speaker-out setup on the widely used KUL dataset. This improvement is due to the use of an improved CNN structure, data augmentation, multitask training, and model combination. The second part, Mamba, is a sequence model first applied to auditory spatial attention decoding to leverage the long-term dependency from previous SWCNN time steps. By joint training SWCNN and Mamba, the proposed SWIM structure uses both short-term and long-term information and achieves an accuracy of 86.2%, which reduces the classification errors by a relative 31.0% compared to the previous state-of-the-art result.
| Type: | Proceedings paper |
|---|---|
| Title: | SWIM: Short-Window CNN Integrated With Mamba for EEG-Based Auditory Spatial Attention Decoding |
| Event: | 2024 IEEE Spoken Language Technology Workshop (SLT) |
| Dates: | 2 Dec 2024 - 5 Dec 2024 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/SLT61566.2024.10832311 |
| Publisher version: | https://doi.org/10.1109/slt61566.2024.10832311 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Training, Accuracy, Source coding, Benchmark testing, Brain modeling, Feature extraction, Data augmentation, Electroencephalography, Data models, Decoding |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Speech, Hearing and Phonetic Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10205071 |
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